DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11 has been entered.
Status of Claims
Claims 1, 3-8, 10-13, 15, and 17-19 are pending. Claims 2, 9, 14, and 16 are canceled.
Response to Arguments
Applicant’s arguments, see p.6-11, filed 11/19/2025, with respect to the rejections of Claims 15 and 17-18 under 35 U.S.C. 103 have been fully considered but are moot because Applicant’s amendments have altered the scope of the claims, and therefore, necessitated new grounds of rejection which are presented below. Applicant’s arguments, see p.6-11, filed 11/19/2025, with respect to the rejections of Claims 1, 3-8, 10-13, and 19 under 35 U.S.C. 103 have been fully considered and are found persuasive. Therefore, the 35 U.S.C. 103 rejection of Claims 1, 3-8, 10-13, and 19 has been withdrawn.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Piat et al. (US 20210090212 A1) in view of Schaefferkoetter (US 20230009528 A1), and Sahbaee et al. (US 20200281543 A1).
Regarding Claim 15, Piat teaches "An apparatus, comprising: processing circuitry configured to receive integration computed tomography (CT) image data of a subject"; (Piat, Paras. 4 and 6, teaches acquiring CT data for the patient wherein the PET and CT scans use integrated reconstruction techniques, i.e., receiving integration CT image data of a subject);
"the integration CT image data being obtained by a non-spectral CT scan"; (Piat, Paras. 6 and 82, teaches acquiring CT data for the patient for generating an attenuation map for PET reconstruction wherein the CT scan provides measures of attenuation of the x-ray energy at different locations such as voxels within the patient, i.e., receiving single-energy CT image data from CT scan as opposed to spectral CT data).
However, Piat does not explicitly teach "and generate an attenuation map for Positron Emission Tomography (PET) image reconstruction by inputting the received integration CT image data to a trained deep convolutional neural network (DCNN) model that outputs the attenuation map; wherein the DCNN model is trained using integration CT image data obtained by a non-spectral CT scan and corresponding training data from spectral CT image data obtained by a spectral CT scan".
In an analogous field of endeavor, Schaefferkoetter teaches "and generate an attenuation map for Positron Emission Tomography (PET) image reconstruction by inputting the received integration CT image data to a trained deep convolutional neural network (DCNN) model that outputs the attenuation map"; (Schaefferkoetter, Abstract and Para. 27, teaches applying a trained deep learning neural network to the PET measurement data and the CT measurement data to generate an attenuation correction map wherein a corrected image is reconstructed based on the generated attenuation map and the PET image, i.e., generating an attenuation map for PET image reconstruction by inputting CT image data into a training neural network that outputs an attenuation map).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Piat by including the generation of an attenuation map for PET reconstruction by inputting the CT image data to a trained deep neural network taught by Schaefferkoetter. One of ordinary skill in the art would be motivated to combine the references since it improves reconstruction (Schaefferkoetter, Para. 21, teaches the motivation of combination to be to improve the reconstruction of images).
However, the combination of references of Piat in view of Schaefferkoetter does not explicitly teach "wherein the DCNN model is trained using integration CT image data obtained by a non-spectral CT scan and corresponding training data from spectral CT image data obtained by a spectral CT scan".
In an analogous field of endeavor, Sahbaee’543 teaches "wherein the DCNN model is trained using integration CT image data obtained by a non-spectral CT scan and corresponding training data from spectral CT image data obtained by a spectral CT scan"; (Sahbaee'543, FIG. 2 and Paras. 35-39, teaches an embodiment for training with machine learning wherein the training data includes samples from sources such as decomposed material maps from spectral CT scans, i.e., corresponding training data from spectral CT image data obtained by a spectral CT scan, and CT-based material decomposition imaging of a phantom with known materials as ground truth, i.e., training model using CT image data obtained by non-spectral CT scans).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Piat and Schaefferkoetter by including the training of the model using non-spectral CT scan data and training data derived from spectral CT scan data taught by Sahbaee’543. One of ordinary skill in the art would be motivated to combine the references since it allows for more accurate material decomposition (Sahbaee'543, Para. 3, teaches the motivation of combination to be to allow for more accurate material decomposition and/or three or more material decomposition).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Piat in view of Schaefferkoetter, Sahbaee’543, and Sahbaee et al. (US 20210093266 A1).
Regarding Claim 17, the combination of references does not explicitly teach "The apparatus of claim 15, wherein the processing circuitry is further configured to: obtain PET data from a scan of a subject; and reconstruct a PET image from the obtained PET data and the generated attenuation map”.
In an analogous field of endeavor, Sahbaee’266 teaches "The apparatus of claim 15, wherein the processing circuitry is further configured to: obtain PET data from a scan of a subject"; (Sahbaee'266, Para. 42, teaches generating a PET image from the PET scan, i.e., obtaining PET data from a scan of the subject);
"and reconstruct a PET image from the obtained PET data and the generated attenuation map"; (Sahbaee'266, Para. 42, teaches using the attenuation to correct the PET data as part of reconstruction, i.e., reconstructing a PET image from the PET data and attenuation map).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Piat, Schaefferkoetter, and Sahbaee’543 by including the obtaining of PET data from a scan of a subject and reconstructing the PET image from the obtained PET data and the generated attenuation map taught by Sahbaee’266. One of ordinary skill in the art would be motivated to combine the references since it improves resolution of the image (Sahbaee'266, Para. 22, teaches the motivation of combination to be to improve spatial resolution of CT image, increase quantitative accuracy, reduce radiation dosages, improve contrast, and eliminate septa).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 18, the combination of references of Piat in view of Schaefferkoetter, Sahbaee’543, and Sahbaee’266 teaches "The apparatus of claim 15, wherein the processing circuitry is further configured to obtain the training attenuation map data from the input image data, which is one of data produced by a spectral CT scan, data obtained from a fast kV switching dual-energy CT scan, and data obtained with a scan using a photon-counting CT apparatus"; (Sahbaee'266, Paras. 28, 41, 61-62, and 65, teaches spectral CT data obtained from kV switching at two or more different energies, a photon counting detector, and using a computational model optimized on attenuation given spectral CT data for a phantom or using a simulation model which creates attenuation maps and respective CT measurements wherein the spectral CT data is used to create ground truth training attenuation map data, i.e., obtaining training attenuation map data from fast kV switching dual energy CT scans, photon counting CT apparatus, or from simulation or phantoms).
The proposed combination as well as the motivation for combining the Piat in view of Schaefferkoetter, Sahbaee’543, and Sahbaee’266 references presented in the rejection of Claim 17, applies to claim 18. Thus, the apparatus recited in claim 18 is met by Piat in view of Schaefferkoetter, Sahbaee’543, and Sahbaee’266.
Allowable Subject Matter
Claims 1, 3-8, 10-13, and 19 are allowed. The following is the examiner’s stated reason for indication of allowable subject matter: Regarding Claim 1, Piat et al. (US 20210090212 A1) teaches "A method, comprising: receiving integration computed tomography (CT) image data of a subject"; (Piat, Paras. 4 and 6, teaches acquiring CT data for the patient wherein the PET and CT scans use integrated reconstruction techniques, i.e., receiving integration CT image data of a subject);
"the integration CT image data being obtained by a non-spectral CT scan";(Piat, Paras. 6 and 82, teaches acquiring CT data for the patient for generating an attenuation map for PET reconstruction wherein the CT scan provides measures of attenuation of the x-ray energy at different locations such as voxels within the patient, i.e., receiving single-energy CT image data from CT scan as opposed to spectral CT data).
In an analogous field of endeavor, Schaefferkoetter (US 20230009528 A1) teaches "and generating an attenuation map for Positron Emission Tomography (PET) image reconstruction by inputting the received integration CT image data into a trained deep convolutional neural network (DCNN) model that outputs the attenuation map";(Schaefferkoetter, Abstract and Para. 27, teaches applying a trained deep learning neural network to the PET measurement data and the CT measurement data to generate an attenuation correction map wherein a corrected image is reconstructed based on the generated attenuation map and the PET image, i.e., generating an attenuation map for PET image reconstruction by inputting CT image data into a training neural network that outputs an attenuation map).
In an analogous field of endeavor, Sahbaee et al. (US 20210093266 A1), Para. 65, teaches generating the 511 KeV attenuation map by a machine-learned model in response to input of the spectral CT data wherein samples of CT measurements and ground truth 511 KeV attenuation maps are used as training data in machine learning in which the machine learns to generate the 511 KeV attenuation in response to input of the CT measurements at different energies as opposed to explicitly training the DCNN model using non-spectral CT image data as well as the attenuation map data generated from spectral CT image data. Additionally, in an analogous field of endeavor, Sahbaee et al. (US 20200281543 A1), FIG. 2 and Paras. 35-38, teaches an embodiment for training with machine learning wherein the training data includes samples from sources such as decomposed material maps from spectral CT scans and CT-based material decomposition imaging of a phantom with known materials as ground truth as opposed to training the DCNN model explicitly with attenuation map data from the spectral CT image data as well as CT image data explicitly obtained by non-spectral CT scans. Therefore, neither Sahbaee’266 nor Sahbaee’543 explicitly teaches "wherein the DCNN model was trained using integration CT image data obtained by a non-spectral CT scan and corresponding training attenuation map data generated from spectral CT image data obtained by a spectral CT scan".
Therefore, none of the cited prior art references alone or in combination teach the ordered combination of limitations of "wherein the DCNN model was trained using integration CT image data obtained by a non-spectral CT scan and corresponding training attenuation map data generated from spectral CT image data obtained by a spectral CT scan" with the rest of the claim limitations. Claims 3-8, 10-13, and 19 are dependent upon Claim 1 and therefore contains the above indicated allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW STEVEN BUDISALICH whose telephone number is (703)756-5568. The examiner can normally be reached Monday - Friday 8:30am-5:00pm EST.
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/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662